AI to Z – all the terms of AI
Artificial intelligence (AI) is becoming ever more prevalent in our lives. It’s no longer confined to certain industries or research institutions; AI is now for everyone.
| Term | Definition |
|---|---|
| Algorithm | A set of instructions given to a computer to solve a problem or perform calculations. |
| Alignment problem | The discrepancy between intended objectives for an AI system and its actual output. |
| Artificial General Intelligence (AGI) | Hypothetical point in the future where AI is expected to match or surpass human cognitive capabilities. |
| Artificial Neural Network (ANN) | Computer algorithms used in deep learning, made up of interconnected nodes that mimic the brain’s neural circuitry. |
| Big data | Datasets that are much more massive and complex than traditional data. |
| Chinese Room | A thought experiment arguing that a computer program can never be conscious or truly understand its behavior. |
| Deep learning | A category of machine learning that uses advanced neural networks to achieve higher accuracy. |
| Diffusion model | An AI model that learns by adding random noise to training data and assessing differences. |
| Explainable AI | Methods for increasing transparency and users’ trust in AI systems. |
| Generative AI | AI systems that generate new content in response to prompts. |
| Labelling | Categorizing data points to help an AI model make sense of the data. |
| Large Language Model (LLM) | AI models trained on massive quantities of unlabelled text, producing human-like responses. |
| Machine learning | Training AI systems to analyze data, learn patterns, and make predictions without specific human instruction. |
| Natural language processing (NLP) | The broader AI field focusing on machines’ ability to learn, understand, and produce human language. |
| Parameters | Settings used to tune machine-learning models. |
| Responsible AI | Developing and deploying AI systems in a human-centered way, adhering to ethical principles. |
| Sentiment analysis | Identifying and interpreting the emotions behind a text. |
| Supervised learning | Using labeled data to train an algorithm to make predictions. |
| Training data | Data used to teach AI systems how to make predictions. |
| Transformer | A type of deep-learning model used primarily in natural language processing tasks. |
| Turing test | A machine intelligence concept to determine if a computer can exhibit human intelligence. |
| Unsupervised learning | Algorithms trained on unlabelled data to explore patterns and discover unidentified patterns for further analysis. |
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